Angular measurements of light scattered by turbid chiral media using linear Stokes polarimeter
Bibliographic record
Abstract
The effects of turbid chiral media on light polarization are studied in different directions around the scattering samples using a refined linear Stokes polarimeter, which simplifies the signal analysis, and allows for the detailed investigations of scattered light. Because no moving parts are involved in a measurement at a specific detection direction, the determination accuracy of polarization states is increased. The results show that light depolarization increases with both turbidity and detection angle for low and moderately turbid samples; however, the angular dependence decreases with increasing turbidity. When the turbidity is increased to approximately 100 cm(-1), the depolarization becomes higher in the forward than in the backward direction. Polarization sensitive Monte Carlo simulations are used to verify some experimental observations. The results also demonstrate that surviving linear polarization fraction and overall intensity are more sensitive to the increase of glucose concentration in backward than in the forward direction in highly turbid media, indicating that backward geometry may be preferable for potential glucose detection in a biomedical context. Comparison measurements with optically inactive glycerol suggest that the refractive index matching effect, and not the chiral nature of the solute, dominates the observed optical rotation engendered by glucose in highly turbid media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".